Discovery of Toxin-Degrading Enzymes with Positive Unlabeled Deep Learning
Dachuan Zhang, Huadong Xing, Dongliang Liu, Mengying Han, Pengli Cai, Huikang Lin, Yu Tian, Yu Tian, Y. Jay Guo, Bin Sun, Yingying Le, Ye Tian, Ye Tian, Aibo Wu, Qian‐Nan Hu
Abstract
Identifying functional enzymes for the catalysis of specific biochemical reactions is a major bottleneck in the de novo design of biosynthesis and biodegradation pathways. Conventional methods based on microbial screening and functional metagenomics require long verification periods and incur high experimental costs; recent data-driven methods apply only to a few common substrates. To enable rapid and high-throughput identification of enzymes for complex and less-studied substrates, we propose a robust enzyme’s substrate promiscuity prediction model based on positive unlabeled learning. Using this model, we identified 15 new degrading enzymes specific for the mycotoxins ochratoxin A and zearalenone, of which six could degrade >90% mycotoxin content within 3 h. We anticipate that this model will serve as a useful tool for identifying new functional enzymes and understanding the nature of biocatalysis, thereby advancing the fields of synthetic biology, metabolic engineering, and pollutant biodegradation.